Control de caudal con un sensor virtual basado en técnicas de deep learning
- González Herbón, Raúl 1
- Rodríguez Ossorio, José Ramón 1
- González Mateos, Guzmán 1
- Morán Álvarez, Antonio 1
- Alonso Castro, Serafín 1
- Fuertes Martínez, Juan José 1
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1
Universidad de León
info
- Carlos Balaguer Bernaldo de Quirós (coord.)
- José Manuel Andújar Márquez (coord.)
- Ramon Costa Castelló (coord.)
- Carlos Ocampo Martínez (coord.)
- Jesús Fernández Lozano (coord.)
- Matilde Santos Peñas (coord.)
- José Enrique Simó Ten (coord.)
- Montserrat Gil Martínez (coord.)
- Jose Luis Calvo Rolle (coord.)
- Raúl Marín Prades (coord.)
- Eduardo Rocón de Lima (coord.)
- Elisabet Estévez Estévez (coord.)
- Pedro Jesús Cabrera Santana (coord.)
- David Muñoz de la Peña Sequedo (coord.)
- José Luis Guzmán Sánchez (coord.)
- José Luis Pitarch Pérez (coord.)
- Oscar Reinoso García (coord.)
- Oscar Déniz Suárez (coord.)
- Emilio Jiménez Macías (coord.)
- Vanesa Loureiro Vázquez (coord.)
Publisher: Servizo de Publicacións ; Universidade da Coruña
ISBN: 978-84-9749-841-8
Year of publication: 2022
Pages: 368-375
Congress: Jornadas de Automática (43. 2022. Logroño)
Type: Conference paper
Abstract
In the field of industrial control, the use of virtual sensors has great interest in cases where it is not possible to have access to the physical variable to be controlled, either because the real sensor is expensive, cannot be installed or is broken-down. This paper proposes the implementation of a flow control loop in an industrial pilot plant, comparing the performance of this loop when the real flow meter is used with the performance when a virtual flow sensor is used. The virtual sensor used has been developed using a recurrent neural network.